Empathy injection for question-answering systems

ABSTRACT

Exemplary methods and devices herein receive an inquiry and automatically analyze words used in the inquiry, potential answers, and data maintained by evidence sources using the computerized device to determine the sensitivity level associated with the inquiry. The sensitivity level associated with the inquiry represents an emotional and cognitive state of the user. Such methods and devices automatically generate at least one follow-up question based on the sensitivity level associated with the inquiry and receive a follow-up response into the computerized device in response to the follow-up question(s). The methods and devices also automatically produce scores for the potential answers using the computerized device based on the inquiry, the follow-up responses, and ratings of the evidence sources. Following this, these methods and devices automatically generate output answers to the inquiry based on the sensitivity level associated with the inquiry using the computerized device.

BACKGROUND

The present disclosure relates to question answering systems and provides methods for returning an answer to a query in an empathetic manner based on a determination that the answer may evoke a negative emotional state.

Computerized devices are used for many purposes to improve the quality of people's lives. In one example, computerized systems can automatically answer questions presented by users in natural language format. Such systems are highly useful because they provide a more natural computer interface, thereby improving the user experience.

SUMMARY

Exemplary methods herein receive an inquiry from a user into a computerized device. Such methods automatically perform an initial analysis of the inquiry using the computerized device to produce potential answers to the inquiry from evidence sources. Also, such methods can automatically obtain biometric information from biometric devices operatively connected to the computerized device.

These methods automatically analyze words used in the inquiry, the potential answers, data maintained by the evidence sources, the biometric information, etc., using the computerized device to determine the sensitivity level associated with the inquiry. For example, these methods can automatically identify words associated with a specific emotional or cognitive state. Such methods can also automatically identify sentence structure associated with a specific emotional or cognitive state, or automatically identify relationships between data maintained by the evidence sources and emotionally significant phenomena. Such methods can also automatically detect negative sentiment associated with one of the potential answers. The sensitivity level associated with the inquiry thus represents an emotional and cognitive state of the user as automatically determined by the computerized device.

Such methods can automatically generate at least one follow-up question based on the sensitivity level associated with the inquiry using the computerized device to refine the potential answers. When generating the follow-up question, these methods can generate relatively more empathetic follow-up questions or relatively less empathetic follow-up questions based on the sensitivity level associated with the inquiry. The relatively more empathetic follow-up questions and the relatively less empathetic follow-up questions are distinguished from each other by the amount of information presented and by the word choice used when presenting the information.

Then, these methods automatically output the follow-up question(s) using the computerized device in response to the inquiry and, in turn, receive at least one follow-up response into the computerized device in response to the follow-up question(s). In one example, these methods can output the follow-up questions using an avatar. The avatar changes appearance and mannerisms based on the sensitivity level associated with the inquiry.

Such methods also automatically produce scores for the potential answers using the computerized device based on the inquiry, the follow-up responses, the ratings of the evidence sources, etc. Following this, these methods automatically generate output answers to the inquiry based on the sensitivity level associated with the inquiry using the computerized device to refine the potential answers. When generating the output answers, such methods generate relatively more empathetic output answers or relatively less empathetic output answers based on the sensitivity level associated with the inquiry. Again, the relatively more empathetic output answers and the relatively less empathetic output answers are distinguished from each other by the amount of information presented and the word choice used when presenting information.

Then, such methods automatically output the output answers, the scores, and links to the evidence sources from the computerized device. Again, these methods can output the answers, the scores, the links, etc., using an avatar. The avatar changes appearance and mannerisms based on the sensitivity level associated with the inquiry. In addition, such methods can output the emotional and cognitive state of the user as automatically determined by the computerized device to individuals other than the user, and can make recommendations to such individuals based on the emotional and cognitive state of the user.

Other methods herein receive an inquiry from a user into a computerized device. Such methods automatically perform an initial analysis of the inquiry using the computerized device to produce potential answers to the inquiry from evidence sources. Also, such methods can automatically obtain biometric information from biometric devices operatively connected to the computerized device.

These methods automatically analyze words used in the inquiry, the potential answers, data maintained by the evidence sources, the biometric information, etc., using the computerized device to determine the sensitivity level associated with the inquiry. For example, these methods can automatically identify words associated with a specific emotional or cognitive state. Such methods can also automatically identify sentence structure associated with a specific emotional or cognitive state, or automatically identify relationships between data maintained by the evidence sources and emotionally significant phenomena. Such methods can also automatically detect negative sentiment associated with one of the potential answers. The sensitivity level associated with the inquiry thus represents an emotional and cognitive state of the user as automatically determined by the computerized device.

Such methods can automatically generate at least one follow-up question based on the sensitivity level associated with the inquiry using the computerized device to refine the potential answers. When generating the follow-up question, these methods can generate relatively more empathetic follow-up questions or relatively less empathetic follow-up questions based on the sensitivity level associated with the inquiry. The relatively more empathetic follow-up questions and the relatively less empathetic follow-up questions are distinguished from each other by the amount of information presented and by the word choice used when presenting the information.

Then, these methods automatically output the follow-up question(s) using the computerized device in response to the inquiry and, in turn, receive at least one follow-up response into the computerized device in response to the follow-up question(s). In one example, these methods can output the follow-up questions using an avatar. The avatar changes appearance and mannerisms based on the sensitivity level associated with the inquiry.

Such methods automatically analyze words used in the follow-up response using the computerized device to refine the sensitivity level associated with the inquiry. Such methods also automatically produce scores for the potential answers using the computerized device based on the inquiry, the follow-up responses, the ratings of the evidence sources, etc.

Following this, these methods automatically generate output answers to the inquiry based on the sensitivity level associated with the inquiry using the computerized device to refine the potential answers. When generating the output answers, such methods generate relatively more empathetic output answers or relatively less empathetic output answers based on the sensitivity level associated with the inquiry. Again, the relatively more empathetic output answers and the relatively less empathetic output answers are distinguished from each other by the amount of information presented and the word choice used when presenting information.

Then, such methods automatically output the output answers, the scores, and links to the evidence sources from the computerized device. Again, these methods can output the answers, the scores, the links, etc., using an avatar. The avatar changes appearance and mannerisms based on the sensitivity level associated with the inquiry. In addition, such methods can output the emotional and cognitive state of the user as automatically determined by the computerized device to individuals other than the user, and can make recommendations to such individuals based on the emotional and cognitive state of the user.

Question-answer systems herein comprise a graphic user interface receiving an inquiry from a user, biometric devices receiving biometric information from the user, and a processor operatively connected to the graphic user interface and biometric devices. The processor automatically performs an initial analysis of the inquiry to produce potential answers to the inquiry from evidence sources.

The processor automatically analyzes words used in the inquiry, the potential answers, data maintained by the evidence sources, the biometric information, etc., to determine the sensitivity level associated with the inquiry. For example, the processor can automatically identify words associated with a specific emotional or cognitive state. Such a processor can also automatically identify sentence structure associated with a specific emotional or cognitive state, or automatically identify relationships between data maintained by the evidence sources and emotionally significant phenomena. The processor can also automatically detect negative sentiment associated with one of the potential answers. The sensitivity level associated with the inquiry thus represents an emotional and cognitive state of the user as automatically determined by the computerized device.

The processor can further generate at least one follow-up question based on the sensitivity level associated with the inquiry to refine the potential answers. When generating the follow-up question(s), the processor can generate relatively more empathetic follow-up questions or relatively less empathetic follow-up questions based on the sensitivity level associated with the inquiry. The relatively more empathetic follow-up questions and the relatively less empathetic follow-up questions are distinguished from each other by the amount of information presented and by the word choice used when presenting the information.

Then, the graphic user interface automatically outputs the follow-up question(s) in response to the inquiry and, in turn, receives at least one follow-up response in response to the follow-up question(s). In one example, the graphic user interface can output the follow-up questions using an avatar. The avatar changes appearance and mannerisms based on the sensitivity level associated with the inquiry.

The processor automatically analyzes words used in the follow-up response to refine the sensitivity level associated with the inquiry. The processor also automatically produces scores for the potential answers based on the inquiry, the follow-up responses, the ratings of the evidence sources, etc.

Following this, the processor automatically generates output answers to the inquiry based on the sensitivity level associated with the inquiry to refine the potential answers. When generating the output answers, the processor generates relatively more empathetic output answers or relatively less empathetic output answers based on the sensitivity level associated with the inquiry. Again, the relatively more empathetic output answers and the relatively less empathetic output answers are distinguished from each other by the amount of information presented and the word choice used when presenting information.

Then, the graphic user interface automatically outputs the output answers, the scores, and links to the evidence sources from the computerized device. Again, the graphic user interface can output the answers, the scores, the links, etc., using an avatar. The avatar changes appearance and mannerisms based on the sensitivity level associated with the inquiry. In addition, the graphic user interface can output the emotional and cognitive state of the user as automatically determined by the computerized device to individuals other than the user, and can make recommendations to such individuals based on the emotional and cognitive state of the user.

In other embodiments, a tangible (non-transitory) computer-readable storage media can tangibly store instructions executable by a computerized device reading such instructions from the tangible computer-readable storage media. Such instructions cause the computerized device to perform methods that receive an inquiry from a user into a computerized device. Such methods automatically perform an initial analysis of the inquiry using the computerized device to produce potential answers to the inquiry from evidence sources. Also, such methods can automatically obtain biometric information from biometric devices operatively connected to the computerized device.

These methods automatically analyze words used in the inquiry, the potential answers, data maintained by the evidence sources, the biometric information, etc., using the computerized device to determine the sensitivity level associated with the inquiry. For example, these methods can automatically identify words associated with a specific emotional or cognitive state. Such methods can also automatically identify sentence structure associated with a specific emotional or cognitive state, or automatically identify relationships between data maintained by the evidence sources and emotionally significant phenomena. Such methods can also automatically detect negative sentiment associated with one of the potential answers. The sensitivity level associated with the inquiry thus represents an emotional and cognitive state of the user as automatically determined by the computerized device.

Such methods can automatically generate at least one follow-up question based on the sensitivity level associated with the inquiry using the computerized device to refine the potential answers. When generating the follow-up question, these methods can generate relatively more empathetic follow-up questions or relatively less empathetic follow-up questions based on the sensitivity level associated with the inquiry. The relatively more empathetic follow-up questions and the relatively less empathetic follow-up questions are distinguished from each other by the amount of information presented and by the word choice used when presenting the information.

Then, these methods automatically output the follow-up question(s) using the computerized device in response to the inquiry and, in turn, receive at least one follow-up response into the computerized device in response to the follow-up question(s). In one example, these methods can output the follow-up questions using an avatar. The avatar changes appearance and mannerisms based on the sensitivity level associated with the inquiry.

Such methods automatically analyze words used in the follow-up response using the computerized device to refine the sensitivity level associated with the inquiry. Such methods also automatically produce scores for the potential answers using the computerized device based on the inquiry, the follow-up responses, the ratings of the evidence sources, etc.

Following this, these methods automatically generate output answers to the inquiry based on the sensitivity level associated with the inquiry using the computerized device to refine the potential answers. When generating the output answers, such methods generate relatively more empathetic output answers or relatively less empathetic output answers based on the sensitivity level associated with the inquiry. Again, the relatively more empathetic output answers and the relatively less empathetic output answers are distinguished from each other by the amount of information presented and the word choice used when presenting information.

Then, such methods automatically output the output answers, the scores, and links to the evidence sources from the computerized device. Again, these methods can output the answers, the scores, the links, etc., using an avatar. The avatar changes appearance and mannerisms based on the sensitivity level associated with the inquiry. In addition, such methods can output the emotional and cognitive state of the user as automatically determined by the computerized device to individuals other than the user, and can make recommendations to such individuals based on the emotional and cognitive state of the user.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein will be better understood from the following detailed description with reference to the drawings, which are not necessarily drawn to scale and in which:

FIG. 1 is a flow diagram illustrating embodiments herein;

FIG. 2 is a flow diagram illustrating embodiments herein;

FIG. 3 is a schematic diagram of a hardware system according to embodiments herein;

FIG. 4 is a schematic diagram of a deployment system according to embodiments herein;

FIG. 5 is a schematic diagram of an integration system according to embodiments herein;

FIG. 6 is a schematic diagram of an on demand system according to embodiments herein;

FIG. 7 is a schematic diagram of a virtual private network system according to embodiments herein; and

FIG. 8 is a schematic diagram of a virtual private network system according to embodiments herein.

DETAILED DESCRIPTION

The systems and methods herein relate to question answering systems. Specifically, the systems and methods herein provide methods for returning an answer to a query in an empathetic manner based on a determination that the answer may evoke a negative emotional state. For example, if a question answering system is developed to provide medical answers to a patient based on the patient's medical information, the system may generate the most correct answer, but not a result the patient wants. In other words, the result may be “unfavorable news.” In a medical context where a doctor is providing “unfavorable news” to a patient the doctor is able to provide the news in an empathetic manner. Therefore, the question answering systems and methods described below provide the ability to detect whether an answer is “unfavorable news” based on many different factors, and deliver the result in an empathetic manner when needed. The systems and methods herein determine whether the answer may invoke a negative user response and, if this occurs, provide the result in an empathetic manner.

In one example, the methods and systems herein are useful with question-and-answering systems in a healthcare setting by automatically assessing a user's emotional and cognitive state when presenting medical information to the user. However, the methods and systems herein are not limited to the healthcare field, and are instead applicable to all question/answer systems that respond to user's inquiries.

Based on the assessment of the user's emotional and cognitive state, the methods and systems herein can 1) tailor information with respect to the use of empathetic language and 2) optionally provide suggestions to specific healthcare professionals based on empathy assessments. Extensions to avatar use in virtual universes are also included in systems herein. The way in which information is presented (e.g., with respect to empathy) can affect the degree to which a patient assimilates sensitive or serious health information and also the way in which the patient communicates health information.

The systems and methods herein work with question/answer systems, i.e., those systems which answer natural language questions by querying data repositories and applying elements of language processing, information retrieval, and machine learning to arrive at a conclusion. Specifically, the systems and methods herein present information or answers that have a higher value than that developed by a medical information repository query alone. The features herein use components that are automatically triggered based on criteria.

Regarding the importance of empathy in information presentation, in traditional physician-patient interactions (e.g., “bedside manner”) the better the relationship in terms of emotional connection, respect, and shared values, the more useful will be the sharing of information (e.g., about a patient's disease) that is assimilated by the patient and also communicated to the healthcare professional. This in turn provides feedback in which the accuracy of diagnosis is improved as well as the patient's sense of well-being. When this relationship is poor, patient and physician distrust can occur, and patients may be less likely to appreciate the information or properly act upon receiving such information. Also, if the patient is in a poor emotional state (such as a state of agitation, apprehension, mistrust, or an acute psychological disturbance due to side effects of their condition, e.g., a drug reaction, post-operative, or post-partum depression, etc.) this can sometimes exacerbate disease symptoms, cause symptoms to deteriorate, and affect information exchange between patient and healthcare agents.

Although computerized question/answer systems are being developed to aid healthcare professionals in making diagnoses, in the future end-users may use similar systems for guidance when selecting healthcare professionals and to gain a high-level understanding of symptoms and options. Today, people frequently make use of information discovered on the Internet when first start seeking an understanding of possible diseases related to themselves, relatives, colleagues and friends. Depending on the personality and background of the individual seeking to gain knowledge of disease and injury processes, such information may be frightening, or may be used inappropriately, etc. For example, some individuals who are nervous and/or depressed may become overly fearful.

With respect to empathy injection, methods and systems disclosed herein provide an enhancement of question/answer systems that will modify their responses to user's queries depending on information associated with the psychological states of users of the system. For example, some users may be able to understand and emotionally handle information related to possible rare diseases, cancer, etc., better than others who should be presented with information in a more empathetic fashion. Empathetic language might include phrases such as, “We know that this disease information can be frightening, but this disease is rare. Just please see your physician.” In addition, the question/answer systems may consider the assessed psychological states as inputs to the system used to suggest diagnoses and treatments to a healthcare provider. For example, when used by a physician, such systems can present information in three columns, with the following preamble: “This patient appears to be suffering acute depression, based on her answers to certain questions. Therefore, the prescribed treatments may be best evaluated based on your further determination of to what extent she is depressed: 1) Severe depressions, 2) Moderate depression or 3) Mild depression. Additionally, you may wish to present the information to her in the following manner.”

Thus, one aspect of this disclosure is a method of computing f(E) (emotional impact of a response). For example, “brain” may be a happy answer if the question is asking what part of the body will benefit from a given nutritional supplement, but a sad answer if asking what part of the body will be damaged by a patient's illness. The systems and methods herein can address this by detecting the emotional content at run time using supporting evidence to conclude that “brain” was the answer to the question being asked (in addition to context-independent information).

Finally, an aspect of drug development and regulatory approval of growing importance is the use of companion diagnostics. A companion diagnostic is a test that is performed on a patient prior to administering certain drugs. The test may, for example, be genetic, and may categorize the patient as being in a specific population of individuals for whom the drug has been approved for certain uses. Without such a test, the drug may not be administered. While companion diagnostics typically do not include an assessment of a drug recipient's cognitive state, it is possible with better availability of formal means to measure such states (such as those described in this disclosure) companion diagnostics may be devised with these states as inputs. Therefore, the systems and methods herein are applicable not only to tailoring the presentation of information from a question/answer system based on the patient's cognitive state, but also generate inputs to a companion diagnostic system as it outputs specific insights for diagnosis and treatment. Thus, the systems and methods herein provide compliance with certain companion diagnostics, based on the observed cognitive states of the users.

An overview of the methods and systems herein is shown in flowchart form in FIG. 1, using a patient and healthcare provider example. In item 300, one or more queries are received (e.g., concerning medical symptoms and conditions) from a user. The systems and methods herein (e.g., the digital physician or DP) analyzes the inputs (textual or otherwise) and determine a useful answer (item 302) e.g., diagnosis, via established question/answer techniques. In item 304, the systems and methods herein analyze and monitor the emotional state (E) of the user (e.g., with user profile/query characteristics, biometrics, analysis of language, questionnaires, reports from nursing staff and family members, food intake information, etc.) to generate f(E) (the emotional impact of the answer).

In item 304, the emotional state or personality type may be estimated by the system accessing a user's profile, by an assessment of natural language used by the person, by biometrics, by a history of previous interactions, by an analysis of word combinations used for similar users etc. A user profile, stored on a device, may specify various patient attributes including personality type, comprehensive medical records, or some subset of attributes such as previous diagnoses and disease state (e.g., advanced vs. mild), user nationality, language, or occupation, and other such patient-specific information. This profile may be queried to aid the system in providing a relevant and/or higher-confidence answer in an appropriate manner. Information associated with persons with known circumstances similar to that of question initiator (e.g., via medical diagnoses, occupation, or expertise) may be also used to create an appropriate empathetic tone of response.

In certain circumstances, the system may not have high confidence that a patient's emotional or cognitive state has been adequately characterized or assessed, and under these circumstances the systems and methods herein apply an active learning component. This active learning component can access specific information about individuals who have been in contact with the patient recently, including, but not limited to, nursing staff members, doctors, family members and other visitors, and can direct to these individuals specific questions aimed at aiding the machine learning system to perform this categorization. In some sense, this represents a collaborative input to the question/answer system. If desired, none of this additional information will be obtained without patient consent or without respect to all relevant privacy rules and regulations. For example, with the patient's informed consent, the system may send an email to a spouse with the inquiry, “Based on an AI assessment of your wife's mental state, it is possible she is acutely depressed. To aid in furthering this assessment, could you indicate if you have observed any of the following: 1) increased agitation, 2) increased sensitivity to stress, or 3) unresponsiveness to normal communication.” Based on these inquiries, the machine learning system can incorporate the active learning component's determinations into its future assessments of the patient or other patients' cognitive or emotional states. Therefore, the methods and systems herein can use the active learning component to access specific information provided by other individuals, modify the existing system for estimating a patient's emotional state, and make recommendations about the patient to those individuals based on the emotional and cognitive state of said patient and the output from the active learning component and the modified system.

In some specific examples, f(E) can be computed in many ways (assuming a particular value for a predetermined threshold value (Thresh)). In one example, a domain expert can create an informal account of characteristics that are predictive of a high emotional state, and an account of what combinations of these characteristics are necessary to conclude a high emotional state. A programmer can then implement f(E) so that it exceeds Thresh as required by the specification. In another example, a domain expert can create a formal account of characteristics that are predictive of a high emotional state, and an account of what combinations of these characteristics are necessary to conclude a high emotional state using a formal rule language. The rule engine is thus able to directly compute f(E).

In additional examples, a domain expert can create an informal account of characteristics that are predictive of a high emotional state, but do not provide an account of what combinations of these characteristics are necessary to conclude a high emotional state. A programmer can implement detectors for each of the characteristics. The system is given training instances of input data (“biometrics, analysis of language, questionnaires, reports from nursing staff and family members, food intake information, etc.”, as noted above) along with manual judgments by experts as to whether each patient in the training set is in a high emotional state. Statistical machine learning techniques are used to construct an f(E) that best combines the results of the detectors to fit the manual judgments in the training data to whether f(E)>Thresh.

The foregoing examples can be combined. Thus, a domain expert can create a formal account of characteristics that are predictive of a high emotional state (but do not provide an account of what combinations of these characteristics are necessary to conclude a high emotional state). The rule engine is thus able to compute values that are relevant to f(E). The system is given training instances of input data along with manual judgments by experts as to whether each patient in the training set is in a high emotional state. Statistical machine learning techniques are used to construct an f(E) that best combines the results of the rule engine to fit the manual judgments in the training data to whether f(E)>Thresh.

One component of systems and methods herein (operating in item 304) includes a manually generated list of answers that are known to be associated with various emotional states in response to various types of questions. This component labels the answer with one or more numerical features indicating the emotional content of the answer. For example, for questions asking for the diagnosis of a patient, answers such as “healthy”, “influenza”, “meningitis”, and “paranoid schizophrenia” all imply different emotional consequences for that patient.

Another component of systems and methods herein (operating in item 304) includes an automatically mined list of terms that occur disproportionately in a corpus of text indicating negative sentiment in a training corpus. This component labels the answer with a score indicating the frequency of that sentiment. For example, if “meningitis” was almost always associated negative sentiment in the corpus, then that answer would be labeled with a numerical feature indicating this relationship.

An additional component of systems and methods herein (operating in item 304) includes a structured knowledge resource containing relationships between entities and emotionally significant phenomena. This component labels answers linked to those phenomena with one or more numerical features indicating the emotional content of the answer. For example, if some ontology indicates that meningitis causes severe headaches, and severe headaches are known to be associated with negative emotions, the answer “meningitis” would be labeled with a feature indicating a relationship to negative emotions.

A further component of systems and methods herein (operating in item 304) includes a manually generated list of keywords that tend to co-occur in passages that suggest a negative emotional state keeps a count of how many times those keywords appear in the supporting passages of that answer. For example, passages containing the words “death” and “pain” may be indicative of the emotional state associated with the answer that this passage appears to support. Another component of systems and methods herein (operating in item 304) detects negative sentiment regarding a candidate answer in a supporting passage and labels that answer with a numerical feature indicating the strength of the negative sentiment.

Also, one component of systems and methods herein (operating in item 304) includes a precompiled model of the emotional states suggested by a passage. This component applies that model to the supporting passage and labels the answer with the conclusion from that model. Such a model is precompiled by manually labeling a set of sample passages as suggesting various emotional states and applying a text classification algorithm to build a model predictive of that manually labeled data. For example, a manually identified set of passages can involve angry patients, and this component uses a classification process to build a model that detects passages involving angry patients. The component then applies that model at question answering time to produce a feature indicative of whether the answer under consideration is suggestive of an angry patient.

Another component of systems and methods herein (operating in item 304) triggers use of one of a multitude of models of the emotional states suggested by a passage (based on a one or more patient-specific features). For example, a patient that is on a particular drug may require a different model than a patient that is not on that drug, especially if the drug is known to have side effects on cognitive processing, such as causing irritability. In such a case the feature indicative of whether the answer is suggestive of an angry patient will depend on the drug as well as the content of the passage.

In item 306, if f(E) (emotional impact of the answer) is greater than a predetermined threshold value (Thresh) then processing proceeds to item 310 to enter a more “empathetic mode.” Otherwise, processing proceeds to item 308, where the question/answer system outputs the answer from item 302 in an unaltered manner.

In item 306, even though a single threshold is given in order to enter the emphatic mode, in other embodiments a continuum value of f(E) can be used to shape the emphatic level of responses of the question/answer system. Here, f is a function. For example, four alternatives for computation of f(E) can be used, and all four can depend on “characteristics that are predictive of a high emotional state.” Some of those characteristics may be available in the system as structured information from a profile and others may be observed by the system during use. A third source of these characteristics is analysis embedded in the question answering system itself.

As noted above, in item 310, the systems and methods herein convey health information to the user using a relatively more empathetic mode (e.g., by altering the answer to use different language or to include different or less information). In item 310, if a user has used natural language with certain words that express anxiety, f(E) may be high, and the empathetic mode is entered in which a more “caring” wording is used for conveying information.

As additional examples of how information is presented in item 310, some studies show that younger patients prefer to be addressed by their first name. Thus, the empathetic mode can use this information when determining the way in which to interact with a user of the system or when providing information. To the contrary, some studies show people older than age 65 may not prefer this, and the empathetic mode can make use of these studies as well.

In item 310, the user interface can be a virtual universe (VU) that uses avatars (human-like or cartoon characters) and the characteristics of the VU and avatars can be made more empathetic depending on f(E). With the avatars of systems and methods herein, the vocal tones and body language can reflect a caring “bedside manner.” Words, tone, volume, avatar appearance, etc., are all parameters that can be affected by empathy. For example, in a virtual universe (VU) of systems and methods herein, avatars can automatically change appearance and vocal characteristics based on f(E). Avatars of systems and methods herein can address users in ways in which information can be assimilated and exchanged. For example, the avatar can chance appearance, based on f(E), with respect to clothing, facial characteristics, height, timbre of voice, gender, etc. Avatars of systems and methods herein can also change motion characteristics (e.g., gestures, pacing, head movements, hand movements, etc., based on f(E). In addition, f(E) can be control the scenery in the VU (e.g., hospital, island, landscape).

Also, item 310 includes a virtual universe switching element. With the virtual switching element the system can alter the modality (e.g., email, instant messaging, avatar in a virtual universe, etc.) in which information if conveyed (based on f(E)). For example, certain information may be conveyed better by an avatar that may gesture to its body, and the system can automatically switch the user to a virtual universe when conveying such information.

In item 312, the systems and methods herein can optionally suggest a possible physician that the user may want to contact for more information (using empathetic mode). In item 312, the systems and methods herein can, for example, suggest healthcare professionals who have been rated as having higher empathy ratings than other healthcare professional.

In item 314, information of the patient's emotional state can be output to healthcare professionals. Thus, the systems and methods herein formulate strategies for healthcare professionals to present unfavorable (or unwelcome) information in such a way that minimizes stress, while benefiting the patient. In addition, in item 314, a suggested tone may be provided in a physician's report, alerting the healthcare provider that the patient is in a specific emotional state (and therefore more empathetic tone should be employed). Also, the assessed emotional state of the patient can be provided as an additional determinant of the diagnosis itself in item 314, suggesting different treatments to the physician depending on these assessed emotional states. Further, in item 314, the systems and methods herein can output information to staff, who may have access to patient rosters, annotated with question/answer suggested tones and levels of empathy. Item 314 can also provide a display (sign) to be used on the door or bedside of the patient, and the sign can provide coded information to all providers who enter the room about the suggested tone and level of empathy. This information is especially useful to nurses, who often interact with patients more than doctors, and therefore can have a greater impact on how a patient's psychological state evolves or deteriorates.

Further, in item 316, an optional user rating system and analysis can be incorporated for auto-tuning the process.

Therefore, systems and methods herein include an emotion-assessment component and an information-output component with information changing based on input from the emotion-assessment component (e.g., if f(E)>Thresh, then enter a more “empathetic mode”). With systems and methods herein, the information can be a suggestion for a healthcare professional, and the information can be an input to the question/answer system and aid in diagnosis and/or prescription of a treatment. Also, the information can be used to change avatar characteristics (e.g., avatar appearance, motions, and vocal characteristics in a virtual universe) and/or scenery (e.g., hospital vs. forest). Additionally, the information can be used to automatically switch the user to a virtual universe. For example, certain information may be conveyed better by an avatar that may gesture to its body.

The function (f(E)) used to control the mode can take the form of a specialized, ad hoc computation, or take the form of a formal logical specification that is interpreted by a general-purpose automated rule engine, and such features can be combined with concrete examples of desired behavior via statistical machine learning.

While the foregoing discusses the systems and methods herein using a patient/healthcare provider example, FIG. 2 is a flowchart illustrating the method and systems herein in a more generic manner applicable to all computerized question/answer systems. In item 350, these methods receive an inquiry or query from a user into a computerized device. Such methods automatically perform an initial analysis of the inquiry using the computerized device to produce potential answers to the inquiry from evidence sources in item 352. Also, such methods can automatically obtain biometric information from biometric devices operatively connected to the computerized device in item 354.

As shown in item 356, these methods automatically analyze words used in the inquiry, the potential answers, data maintained by the evidence sources, the biometric information, etc., using the computerized device to determine the sensitivity level associated with the inquiry, in item 358. For example, in item 356 these methods can automatically identify words associated with a specific emotional or cognitive state. Such methods can also automatically identify sentence structure associated with a specific emotional or cognitive state, or automatically identify relationships between data maintained by the evidence sources and emotionally significant phenomena. Such methods can also automatically detect negative sentiment associated with one of the potential answers. The sensitivity level associated with the inquiry 358 thus represents an emotional and cognitive state of the user as automatically determined by the computerized device.

In item 360, such methods can automatically generate at least one follow-up question based on the sensitivity level associated with the inquiry using the computerized device to refine the potential answers. When generating the follow-up question in item 360, these methods can generate relatively more empathetic follow-up questions or relatively less empathetic follow-up questions based on the sensitivity level associated with the inquiry. The relatively more empathetic follow-up questions and the relatively less empathetic follow-up questions are distinguished from each other by the amount of information presented and by the word choice used when presenting the information.

Then, these methods automatically output the follow-up question(s) using the computerized device in response to the inquiry and, in turn, receive at least one follow-up response into the computerized device in response to the follow-up question(s) in item 362. In one example, these methods can output the follow-up questions using an avatar. The avatar changes appearance and mannerisms based on the sensitivity level associated with the inquiry.

As shown in item 364, such methods automatically analyze words used in the follow-up response using the computerized device to refine the sensitivity level associated with the inquiry. Such methods also automatically produce scores for the potential answers using the computerized device based on the inquiry, the follow-up responses, the ratings of the evidence sources, etc. In item 366, these methods determine whether the sensitivity level (again represented by the function f(E)) exceeds the predetermined threshold (Thresh).

Following this, these methods automatically generate output answers to the inquiry based on the sensitivity level associated with the inquiry using the computerized device to refine the potential answers. When generating the output answers, based on the sensitivity level associated with the inquiry, such methods generate and output relatively more empathetic output answers in item 370 if the function f(E)) exceeds the predetermined threshold (Thresh); or generate and output relatively less empathetic output answers in item 368 if the function f(E)) does not exceed the predetermined threshold (Thresh). Again, the relatively more empathetic output answers and the relatively less empathetic output answers are distinguished from each other by the amount of information presented and the word choice used when presenting information.

With the answers in items 368 and 370, such methods automatically output the scores and links to the evidence sources from the computerized device. Again, in items 368 and 370, these methods can output the answers, the scores, the links, etc., using an avatar. In addition to providing different forms of answers, the avatar changes appearance, background, mannerisms, etc., based on the sensitivity level associated with the inquiry. In addition, such methods can output the emotional and cognitive state of the user as automatically determined by the computerized device to individuals other than the user, and can make recommendations to such individuals based on the emotional and cognitive state of the user, in item 372.

Question-answer systems are illustrated in the following drawings. Such systems comprise a graphic user interface receiving an inquiry from a user, biometric devices receiving biometric information from the user, and a processor operatively connected to the graphic user interface and biometric devices. The processor automatically performs an initial analysis of the inquiry to produce potential answers to the inquiry from evidence sources.

The processor automatically analyzes words used in the inquiry, the potential answers, data maintained by the evidence sources, the biometric information, etc., to determine the sensitivity level associated with the inquiry. For example, the processor can automatically identify words associated with a specific emotional or cognitive state. Such a processor can also automatically identify sentence structure associated with a specific emotional or cognitive state, or automatically identify relationships between data maintained by the evidence sources and emotionally significant phenomena. The processor can also automatically detect negative sentiment associated with one of the potential answers. The sensitivity level associated with the inquiry thus represents an emotional and cognitive state of the user as automatically determined by the computerized device.

The processor can further generate at least one follow-up question based on the sensitivity level associated with the inquiry to refine the potential answers. When generating the follow-up question(s), the processor can generate relatively more empathetic follow-up questions or relatively less empathetic follow-up questions based on the sensitivity level associated with the inquiry. The relatively more empathetic follow-up questions and the relatively less empathetic follow-up questions are distinguished from each other by the amount of information presented and by the word choice used when presenting the information.

Then, the graphic user interface automatically outputs the follow-up question(s) in response to the inquiry and, in turn, receives at least one follow-up response in response to the follow-up question(s). In one example, the graphic user interface can output the follow-up questions using an avatar. The avatar changes appearance and mannerisms based on the sensitivity level associated with the inquiry.

The processor automatically analyzes words used in the follow-up response to refine the sensitivity level associated with the inquiry. The processor also automatically produces scores for the potential answers based on the inquiry, the follow-up responses, the ratings of the evidence sources, etc.

Following this, the processor automatically generates output answers to the inquiry based on the sensitivity level associated with the inquiry to refine the potential answers. When generating the output answers, the processor generates relatively more empathetic output answers or relatively less empathetic output answers based on the sensitivity level associated with the inquiry. Again, the relatively more empathetic output answers and the relatively less empathetic output answers are distinguished from each other by the amount of information presented and the word choice used when presenting information.

Then, the graphic user interface automatically outputs the output answers, the scores, and links to the evidence sources from the computerized device. Again, the graphic user interface can output the answers, the scores, the links, etc., using an avatar. The avatar changes appearance and mannerisms based on the sensitivity level associated with the inquiry. In addition, the graphic user interface can output the emotional and cognitive state of the user as automatically determined by the computerized device to individuals other than the user, and can make recommendations to such individuals based on the emotional and cognitive state of the user.

As will be appreciated by one skilled in the art, aspects of the systems and methods herein may be embodied as a system, method or computer program product. Accordingly, aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable non-transitory medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The non-transitory computer storage medium stores instructions, and a processor executes the instructions to perform the methods described herein. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

Aspects of the present disclosure are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments herein. It will be understood that each block of the flowchart illustrations and/or two-dimensional block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

A representative hardware environment for practicing the embodiments herein is depicted in FIG. 3. This schematic drawing illustrates a hardware configuration of an information handling/computer system in accordance with the embodiments herein. The system comprises at least one processor or central processing unit (CPU) 10. The CPUs 10 are interconnected via system bus 12 to various devices such as a random access memory (RAM) 14, read-only memory (ROM) 16, and an input/output (I/O) adapter 18. The I/O adapter 18 can connect to peripheral devices, such as disk units 11 and tape drives 13, or other program storage devices that are readable by the system. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein. The system further includes a user interface adapter 19 that connects a keyboard 15, mouse 17, speaker 24, microphone 22, and/or other user interface devices such as a touch screen device (not shown) to the bus 12 to gather user input. Additionally, a communication adapter 20 connects the bus 12 to a data processing network 25, and a display adapter 21 connects the bus 12 to a display device 23 which may be embodied as an output device such as a monitor, printer, or transmitter, for example.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments herein. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Deployment types include loading directly in the client, server and proxy computers via loading a storage medium such as a CD, DVD, etc. The process software may also be automatically or semi-automatically deployed into a computer system by sending the process software to a central server or a group of central servers. The process software is then downloaded into the client computers that will execute the process software. The process software is sent directly to the client system via e-mail. The process software is then either detached to a directory or loaded into a directory by a button on the e-mail that executes a program that detaches the process software into a directory. Send the process software directly to a directory on the client computer hard drive. When there are proxy servers, the process will, select the proxy server code, determine on which computers to place the proxy servers' code, transmit the proxy server code, and then install the proxy server code on the proxy computer. The process software will be transmitted to the proxy server then stored on the proxy server.

While it is understood that the process software may be deployed by manually loading directly in the client, server and proxy computers via loading a storage medium such as a CD, DVD, etc., the process software may also be automatically or semi-automatically deployed into a computer system by sending the process software to a central server or a group of central servers. The process software is then downloaded into the client computers that will execute the process software. Alternatively, the process software is sent directly to the client system via e-mail. The process software is then either detached to a directory or loaded into a directory by a button on the e-mail that executes a program that detaches the process software into a directory. Another alternative is to send the process software directly to a directory on the client computer hard drive. When there are proxy servers, the process will, select the proxy server code, determine on which computers to place the proxy servers' code, transmit the proxy server code, then install the proxy server code on the proxy computer. The process software will be transmitted to the proxy server then stored on the proxy server.

In FIG. 4, Step 100 begins the deployment of the process software. The first thing is to determine if there are any programs that will reside on a server or servers when the process software is executed 101. If this is the case then the servers that will contain the executables are identified 209. The process software for the server or servers is transferred directly to the servers' storage via FTP or some other protocol or by copying through the use of a shared file system 210. The process software is then installed on the servers 211.

Next, a determination is made on whether the process software is be deployed by having users access the process software on a server or servers 102. If the users are to access the process software on servers then the server addresses that will store the process software are identified 103.

A determination is made if a proxy server is to be built 200 to store the process software. A proxy server is a server that sits between a client application, such as a Web browser, and a real server. It intercepts all requests to the real server to see if it can fulfill the requests itself. If not, it forwards the request to the real server. The two primary benefits of a proxy server are to improve performance and to filter requests. If a proxy server is required, then the proxy server is installed 201. The process software is sent to the servers either via a protocol such as FTP or it is copied directly from the source files to the server files via file sharing 202. Another embodiment would be to send a transaction to the servers that contained the process software and have the server process the transaction, then receive and copy the process software to the server's file system. Once the process software is stored at the servers, the users via their client computers, then access the process software on the servers and copy to their client computers file systems 203. Another embodiment is to have the servers automatically copy the process software to each client and then run the installation program for the process software at each client computer. The user executes the program that installs the process software on his client computer 212, then exits the process 108.

In step 104 a determination is made whether the process software is to be deployed by sending the process software to users via e-mail. The set of users where the process software will be deployed are identified together with the addresses of the user client computers 105. The process software is sent via e-mail to each of the users' client computers 204. The users then receive the e-mail 205 and then detach the process software from the e-mail to a directory on their client computers 206. The user executes the program that installs the process software on his client computer 212, then exits the process 108.

Lastly, a determination is made on whether to the process software will be sent directly to user directories on their client computers 106. If so, the user directories are identified 107. The process software is transferred directly to the user's client computer directory 207. This can be done in several ways such as but not limited to sharing of the file system directories and then copying from the sender's file system to the recipient user's file system or alternatively using a transfer protocol such as File Transfer Protocol (FTP). The users access the directories on their client file systems in preparation for installing the process software 208. The user executes the program that installs the process software on his client computer 212 then exits the process 108.

The process software is integrated into a client, server and network environment by providing for the process software to coexist with applications, operating systems and network operating systems software and then installing the process software on the clients and servers in the environment where the process software will function.

The first step is to identify any software on the clients and servers including the network operating system where the process software will be deployed that are required by the process software or that work in conjunction with the process software. This includes the network operating system that is software that enhances a basic operating system by adding networking features.

Next, the software applications and version numbers will be identified and compared to the list of software applications and version numbers that have been tested to work with the process software. Those software applications that are missing or that do not match the correct version will be upgraded with the correct version numbers. Program instructions that pass parameters from the process software to the software applications will be checked to ensure the parameter lists matches the parameter lists required by the process software. Conversely, parameters passed by the software applications to the process software will be checked to ensure the parameters match the parameters required by the process software. The client and server operating systems including the network operating systems will be identified and compared to the list of operating systems, version numbers and network software that have been tested to work with the process software. Those operating systems, version numbers and network software that do not match the list of tested operating systems and version numbers will be upgraded on the clients and servers to the required level.

After ensuring that the software, where the process software is to be deployed, is at the correct version level that has been tested to work with the process software, the integration is completed by installing the process software on the clients and servers.

In FIG. 5, step 220 begins the integration of the process software. The first thing is to determine if there are any process software programs that will execute on a server or servers 221. If this is not the case, then integration proceeds to 227. If this is the case, then the server addresses are identified 222. The servers are checked to see if they contain software that includes the operating system (OS), applications, and network operating systems (NOS), together with their version numbers, that have been tested with the process software 223. The servers are also checked to determine if there is any missing software that is required by the process software 223.

A determination is made if the version numbers match the version numbers of OS, applications and NOS that have been tested with the process software 224. If all of the versions match and there is no missing required software the integration continues in 227.

If one or more of the version numbers do not match, then the unmatched versions are updated on the server or servers with the correct versions 225. Additionally, if there is missing required software, then it is updated on the server or servers 225. The server integration is completed by installing the process software 226.

Step 227 which follows either 221, 224, or 226, determines if there are any programs of the process software that will execute on the clients. If no process software programs execute on the clients, the integration proceeds to 230 and exits. If this not the case, then the client addresses are identified 228.

The clients are checked to see if they contain software that includes the operating system (OS), applications, and network operating systems (NOS), together with their version numbers, that have been tested with the process software 229. The clients are also checked to determine if there is any missing software that is required by the process software 229.

A determination is made as to whether the version numbers match the version numbers of OS, applications and NOS that have been tested with the process software 231. If all of the versions match and there is no missing required software, then the integration proceeds to 230 and exits.

If one or more of the version numbers do not match, then the unmatched versions are updated on the clients with the correct versions 232. In addition, if there is missing required software then it is updated on the clients 232. The client integration is completed by installing the process software on the clients 233. The integration proceeds to 230 and exits.

The process software can be stored on a shared file system accessible from one or more servers. The process software is executed via transactions that contain data and server processing requests that use CPU units on the accessed server. CPU units are units of time such as minutes, seconds, hours on the central processor of the server. Additionally, the assessed server may make requests of other servers that require CPU units. CPU units are an example that represents but one measurement of use. Other measurements of use include, but are not limited to, network bandwidth, memory usage, storage usage, packet transfers, complete transactions etc. When multiple customers use the same process software application, their transactions are differentiated by the parameters included in the transactions that identify the unique customer and the type of service for that customer. All of the CPU units and other measurements of use that are used for the services for each customer are recorded. When the number of transactions to any one server reaches a number that begins to affect the performance of that server, other servers are accessed to increase the capacity and to share the workload. Likewise, when other measurements of use such as network bandwidth, memory usage, storage usage, etc., approach a capacity so as to affect performance, additional network bandwidth, memory usage, storage etc., are added to share the workload. The measurements of use used for each service and customer are sent to a collecting server that sums the measurements of use for each customer for each service that was processed anywhere in the network of servers that provide the shared execution of the process software. The summed measurements of use units are periodically multiplied by unit costs and the resulting total process software application service costs are alternatively sent to the customer and or indicated on a web site accessed by the customer, which then remits payment to the service provider. In another embodiment, the service provider requests payment directly from a customer account at a banking or financial institution. In another embodiment, if the service provider is also a customer of the customer that uses the process software application, the payment owed to the service provider is reconciled to the payment owed by the service provider to minimize the transfer of payments.

The process software is shared, simultaneously serving multiple customers in a flexible, automated fashion. It is standardized, requiring little customization and it is scalable, providing capacity on demand in a pay-as-you-go model.

In FIG. 6, step 240 begins the On Demand process. A transaction is created than contains the unique customer identification, the requested service type and any service parameters that further specify the type of service 241. The transaction is then sent to the main server 242. In an On Demand environment the main server can initially be the only server, then as capacity is consumed, other servers are added to the On Demand environment.

The server central processing unit (CPU) capacities in the On Demand environment are queried 243. The CPU requirement of the transaction is estimated, then the servers available CPU capacity in the On Demand environment are compared to the transaction CPU requirement to see if there is sufficient CPU available capacity in any server to process the transaction 244. If there is not sufficient server CPU available capacity, then additional server CPU capacity is allocated to process the transaction 248. If there was already sufficient Available CPU capacity, then the transaction is sent to a selected server 245.

Before executing the transaction, a check is made of the remaining On Demand environment to determine if the environment has sufficient available capacity for processing the transaction. This environment capacity consists of such things as but not limited to network bandwidth, processor memory, storage, etc., 246. If there is not sufficient available capacity, then capacity will be added to the On Demand environment 247. Next, the required software to process the transaction is accessed, loaded into memory, then the transaction is executed 249.

The usage measurements are recorded 250. The usage measurements consist of the portions of those functions in the On Demand environment that are used to process the transaction. The usage of such functions is, but not limited to, network bandwidth, processor memory, storage and CPU cycles are what is recorded. The usage measurements are summed, multiplied by unit costs, and then recorded as a charge to the requesting customer 251. If the customer has requested that the On Demand costs be posted to a web site 252, then they are posted 253.

If the customer has requested that the On Demand costs be sent via e-mail to a customer address 254, then they are sent 255. If the customer has requested that the On Demand costs be paid directly from a customer account 256, then payment is received directly from the customer account 257. The last step is to exit the On Demand process 258.

The process software may be deployed, accessed and executed through the use of a virtual private network (VPN), which is any combination of technologies that can be used to secure a connection through an otherwise unsecured or untrusted network. The use of VPNs is to improve security and for reduced operational costs. The VPN makes use of a public network, usually the Internet, to connect remote sites or users together. Instead of using a dedicated, real-world connection such as leased line, the VPN uses “virtual” connections routed through the Internet from the company's private network to the remote site or employee.

The process software may be deployed, accessed and executed through either a remote-access or a site-to-site VPN. When using the remote-access VPNs the process software is deployed, accessed and executed via the secure, encrypted connections between a company's private network and remote users through a third-party service provider. The enterprise service provider (ESP) sets a network access server (NAS) and provides the remote users with desktop client software for their computers. The telecommuters can then dial a toll-free number or attach directly via a cable or DSL modem to reach the NAS and use their VPN client software to access the corporate network and to access, download and execute the process software.

When using the site-to-site VPN, the process software is deployed, accessed and executed through the use of dedicated equipment and large-scale encryption that are used to connect a companies multiple fixed sites over a public network such as the Internet.

The process software is transported over the VPN via tunneling which is the process of placing an entire packet within another packet and sending it over a network. The protocol of the outer packet is understood by the network and both points, called tunnel interfaces, where the packet enters and exits the network.

In FIGS. 7 and 8, step 260 begins the Virtual Private Network (VPN) process. A determination is made to see if a VPN for remote access is required 261. If it is not required, then proceed to 262. If it is required, then determine if the remote access VPN exists 264.

If it does exist, then proceed to 265. Otherwise, identify the third party provider that will provide the secure, encrypted connections between the company's private network and the company's remote users 276. The company's remote users are identified 277. The third party provider then sets up a network access server (NAS) 278 that allows the remote users to dial a toll free number or attach directly via a cable or DSL modem to access, download and install the desktop client software for the remote-access VPN 279.

After the remote access VPN has been built, or if it been previously installed, the remote users can then access the process software by dialing into the NAS or attaching directly via a cable or DSL modem into the NAS 265. This allows entry into the corporate network where the process software is accessed 266. The process software is transported to the remote user's desktop over the network via tunneling 273. The process software is divided into packets and each packet including the data and protocol is placed within another packet 267. When the process software arrives at the remote user's desktop, it is removed from the packets, reconstituted and then is executed on the remote users desktop 268.

A determination is made to see if a VPN for site to site access is required 262. If it is not required, then proceed to exit the process 263. Otherwise, determine if the site to site VPN exists 269. If it does exist, then proceed to 272. Otherwise, install the dedicated equipment required to establish a site to site VPN 270. Then build the large scale encryption into the VPN 271.

After the site to site VPN has been built, or if it had been previously established, the users access the process software via the VPN 272. The process software is transported to the site users over the network via tunneling. That is, the process software is divided into packets and each packet including the data and protocol is placed within another packet 274. When the process software arrives at the remote user's desktop, it is removed from the packets, reconstituted and is executed on the site users desktop 275. Proceed to exit the process 263.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The descriptions of the various embodiments of the present systems and methods herein have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 

1. A method comprising: receiving an inquiry from a user into a computerized device; automatically performing an initial analysis of said inquiry using said computerized device to produce potential answers to said inquiry from evidence sources; automatically analyzing words used in said inquiry, said potential answers, and data maintained by said evidence sources using said computerized device to determine a sensitivity level associated with said inquiry, said sensitivity level associated with said inquiry representing an emotional and cognitive state of said user as automatically determined by said computerized device; and automatically generating at least one response to said inquiry, said response being one of relatively more empathetic and relatively less empathetic based on said sensitivity level associated with said inquiry.
 2. The method according to claim 1, said response comprising at least one follow-up question, said method further comprising: automatically outputting said at least one follow-up question using said computerized device in response to said inquiry; receiving a follow-up response into said computerized device in response to said at least one follow-up question; automatically producing scores for said potential answers using said computerized device based on said inquiry, said follow-up response, and ratings of said evidence sources; automatically generating output answers to said inquiry based on said sensitivity level associated with said inquiry using said computerized device to refine said potential answers, said generating of said output answers generating one of relatively more empathetic output answers and relatively less empathetic output answers based on said sensitivity level associated with said inquiry; and automatically outputting said output answers, said scores, and links to said evidence sources from said computerized device.
 3. The method according to claim 2, said relatively more empathetic output answers and said relatively less empathetic output answers being distinguished from each other by an amount of information presented and a word choice when presenting information.
 4. The method according to claim 1, said method determining said sensitivity level associated with said inquiry based on at least one of: a manually generated list of responses that are known to be associated with various emotional states in response to various types of questions; an automatically mined list of terms that occur disproportionately in said response indicating negative sentiment; a structured knowledge resource containing relationships between entities and emotionally significant phenomena; a manually generated list of keywords that tend to co-occur in said response that suggest a negative emotional state and keeping a count of how many times said keywords appear in said response; a precompiled model of the emotional states suggested by said response; and a plurality of models of emotional states suggested by said response based on a one or more patient-specific features.
 5. The method according to claim 1, further comprising: outputting said emotional and cognitive state of said user as automatically determined by said computerized device to individuals other than said user; and making recommendations to said individuals based on said emotional and cognitive state of said user.
 6. The method according to claim 1, said analyzing words used in said inquiry, said potential answers, and data maintained by said evidence sources comprising at least one of: automatically identifying words associated with a specific emotional and cognitive state; automatically identifying sentence structure associated with a specific emotional and cognitive state; automatically identifying relationships between data maintained by said evidence sources and emotionally significant phenomena; and automatically detecting negative sentiment associated with one of said potential answers.
 7. A method comprising: receiving an inquiry from a user into a computerized device; automatically performing an initial analysis of said inquiry using said computerized device to produce potential answers to said inquiry from evidence sources; automatically analyzing words used in said inquiry, said potential answers, and data maintained by said evidence sources using said computerized device to determine a sensitivity level associated with said inquiry, said sensitivity level associated with said inquiry representing an emotional and cognitive state of said user as automatically determined by said computerized device; automatically generating at least one follow-up question based on said sensitivity level associated with said inquiry using said computerized device to refine said potential answers, said generating of said follow-up question generating one of relatively more empathetic follow-up questions and relatively less empathetic follow-up questions based on said sensitivity level associated with said inquiry; automatically outputting said at least one follow-up question using said computerized device in response to said inquiry; receiving a follow-up response into said computerized device in response to said at least one follow-up question; automatically analyzing words used in said follow-up response using said computerized device to refine said sensitivity level associated with said inquiry; automatically producing scores for said potential answers using said computerized device based on said inquiry, said follow-up response, and ratings of said evidence sources; automatically generating output answers to said inquiry based on said sensitivity level associated with said inquiry using said computerized device to refine said potential answers, said generating of said output answers generating one of relatively more empathetic output answers and relatively less empathetic output answers based on said sensitivity level associated with said inquiry; and automatically outputting said output answers, said scores, and links to said evidence sources from said computerized device.
 8. The method according to claim 7, further comprising automatically obtaining biometric information from biometric devices operatively connected to said computerized device, said computerized device using said biometric information when determining said sensitivity level associated with said inquiry.
 9. The method according to claim 7, said relatively more empathetic follow-up questions and said relatively less empathetic follow-up questions being distinguished from each other by an amount of information presented and a word choice when presenting information; and said relatively more empathetic output answers and said relatively less empathetic output answers being distinguished from each other by an amount of information presented and a word choice when presenting information.
 10. The method according to claim 7, said outputting of said output answers comprising automatically switching to a virtual universe and using an avatar, said avatar changing appearance and mannerisms based on said sensitivity level associated with said inquiry.
 11. The method according to claim 7, further comprising: outputting said emotional and cognitive state of said user as automatically determined by said computerized device to individuals other than said user; using an active learning component to access specific information from said individuals; and making recommendations to said individuals based on said emotional and cognitive state of said user and output from said active learning component.
 12. The method according to claim 7, said analyzing words used in said inquiry, said potential answers, and data maintained by said evidence sources comprising at least one of: automatically identifying words associated with a specific emotional and cognitive state; automatically identifying sentence structure associated with a specific emotional and cognitive state; automatically identifying relationships between data maintained by said evidence sources and emotionally significant phenomena; and automatically detecting negative sentiment associated with one of said potential answers. 13-18. (canceled)
 19. A method comprising: receiving an inquiry from a user into a computerized device; automatically performing an initial analysis of said inquiry using said computerized device to produce potential answers to said inquiry from evidence sources; automatically analyzing words used in said inquiry, said potential answers, and data maintained by said evidence sources using said computerized device to determine a sensitivity level associated with said inquiry, said sensitivity level associated with said inquiry representing an emotional and cognitive state of said user as automatically determined by said computerized device; automatically generating at least one follow-up question based on said sensitivity level associated with said inquiry using said computerized device to refine said potential answers; automatically outputting said at least one follow-up question using said computerized device in response to said inquiry; receiving a follow-up response into said computerized device in response to said at least one follow-up question; automatically analyzing words used in said follow-up response using said computerized device to refine said sensitivity level associated with said inquiry; automatically producing scores for said potential answers using said computerized device based on said inquiry, said follow-up response, and ratings of said evidence sources; and automatically generating output answers to said inquiry based on said sensitivity level associated with said inquiry using said computerized device to refine said potential answers, said generating of said output answers generating one of relatively more empathetic output answers and relatively less empathetic output answers based on said sensitivity level associated with said inquiry.
 20. The method according to claim 19, further comprising automatically obtaining biometric information from biometric devices operatively connected to said computerized device, said computerized device using said biometric information when determining said sensitivity level associated with said inquiry.
 21. The method according to claim 19, said relatively more empathetic output answers and said relatively less empathetic output answers being distinguished from each other by an amount of information presented and a word choice when presenting information.
 22. The method according to claim 19, said outputting of said output answers comprising automatically switching to a virtual universe and using an avatar, said avatar changing appearance and mannerisms based on said sensitivity level associated with said inquiry.
 23. The method according to claim 19, further comprising: outputting said emotional and cognitive state of said user as automatically determined by said computerized device to individuals other than said user; using an active learning component to access specific information from said individuals; and making recommendations to said individuals based on said emotional and cognitive state of said user and output from said active learning component.
 24. The method according to claim 19, said analyzing words used in said inquiry, said potential answers, and data maintained by said evidence sources comprising at least one of: automatically identifying words associated with a specific emotional and cognitive state; automatically identifying sentence structure associated with a specific emotional and cognitive state; automatically identifying relationships between data maintained by said evidence sources and emotionally significant phenomena; and automatically detecting negative sentiment associated with one of said potential answers. 